Towards automated molecular detection through simulated generation of CMOS-based rotational spectroscopy

Area of Science:

• Spectroscopy
• Sensor Technology
• Computational Chemistry

Background:

• Complementary Metal-Oxide-Semiconductor (CMOS) sensors offer a low-cost approach to rotational spectroscopy for gas sensing and molecular identification.
• However, noise inherent in CMOS sensor data significantly hinders the effectiveness of molecular identification techniques.

Purpose of the Study:

• To develop a software tool for characterizing noise in CMOS spectroscopy data.
• To synthesize realistic CMOS-generated spectroscopy files for evaluating gas sensing algorithms.
• To assess and propose improvements for spectral matching algorithms in the context of CMOS sensor noise.

Main Methods:

• Development of a software application to characterize noise sources in CMOS sensor samples.
• Synthesis of a large dataset of plausible CMOS-generated rotational spectroscopy files using existing databases.
• Evaluation of traditional spectral matching algorithms on the synthesized dataset.
• Analysis of noise impact on peak finding and spectral matching.

Main Results:

• Successful characterization of noise types in CMOS spectroscopy samples.
• Creation of a comprehensive synthetic dataset of CMOS-generated gas spectra.
• Demonstration of the impact of CMOS noise on spectral matching algorithm performance.
• Identification of potential modifications to algorithms to mitigate noise effects.

Conclusions:

• The developed software tool effectively demonstrates the feasibility and reliability of detection using CMOS sensor samples.
• The synthesized dataset provides a valuable resource for evaluating and improving spectral matching algorithms for low-cost gas sensing.
• Adaptations in peak finding and spectral matching algorithms are crucial for robust molecular identification with CMOS rotational spectroscopy.